Rubrex designs evaluation systems and runs the execution layer for AI teams that take quality seriously.
“Most AI teams can't agree on what a good output looks like. Retraining cycles run without clear signal. Quality is discussed in opinions, not numbers.”
Teams that don't yet have a measurement system
We define what good means for your model, build the rubrics, establish scoring criteria, and create the evaluation infrastructure your team can actually use.
You move from subjective quality assessment to structured, repeatable, measurable evaluation.
Teams that know what to evaluate and need it done
We deploy trained evaluators, run preference comparisons and output grading, and deliver structured datasets aligned to your retraining schedule.
You get high-quality human feedback at scale without building an internal team.
We understand your model, use case, and where quality breaks down.
We build your evaluation framework or align our execution to your existing one.
We run the feedback pipeline and deliver structured, training-ready data.
We work with AI teams that take model quality seriously. If you're building, fine-tuning, or scaling a model and need a structured way to measure and improve it — that's us.
We don't label at random. Every engagement starts with a structured definition of quality.
Senior oversight on every project. You work with people who understand evaluation, not a crowd platform.
Our methodology comes from running RLHF pipelines and evaluation design across frontier models.
Every system we build is tied to a metric. You always know if things are improving.
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